AI LEAD

Role: AI LEAD / Architect
Location: Atlanta, GA- 5days Onsite
Duration: Long term
Client: Synergech

ABOUT SYNERGECH

Synergech Technology Solutions Inc. is a AI-native technology company building enterprise-grade AI platforms and intelligent engineering solutions. We are building an AI-native engineering and technology services model focused on transforming how enterprises build, modernize, and operate software systems — delivering intelligent automation, AI-powered workflows, and platform-level AI capabilities to clients in the insurance and financial services sectors.

Our flagship products — IntelliDoc (AI-powered document intelligence) and Infra0 (GenAI-driven cloud infrastructure provisioning) represent our commitment to building category-defining AI solutions. We are actively transforming into a fully AI-enabled workforce, embedding AI across engineering, delivery, and operations.

 

ROLE OVERVIEW
We are seeking an experienced Lead Agentic AI Engineer to design, build, and scale agentic AI workflows for enterprise platforms, intelligent processes, and AI-driven client solutions. In this hands-on leadership role, you will architect multi-agent systems, integrate enterprise-grade LLM capabilities across Azure AI Foundry, OpenAI, and Anthropic Claude, and deliver production-ready AI solutions that meet the strict compliance and reliability standards of the insurance and financial services industry.

This is a highly hands-on technical leadership role where you will influence architecture, engineering practices, platform direction, and delivery execution.

 

KEY RESPONSIBILITIES
Agentic Workflow Design & Development

• Architect and implement multi-agent systems using frameworks such as LangGraph, Semantic Kernel, AutoGen, or CrewAI

• Design agent orchestration patterns including task decomposition, tool use, context management, memory, and human-in-the-loop (HITL) flows

• Build reliable agentic pipelines handling document extraction, reasoning, routing, and structured output generation

• Implement emerging agentic protocols including MCP (Model Context Protocol), Agent-to-Agent (A2A), AG-UI, and CodeAct Code Interpreter patterns

• Design and evaluate agent skills, manage agent harnesses, and maintain agent capability registries

• Design AI solutions capable of leveraging multiple LLM ecosystems including Azure OpenAI, OpenAI, Anthropic Claude, and open-source models based on workload characteristics, governance requirements, and cost/performance considerations

 

Full-Stack AI Application Engineering
• Build full-stack AI-native applications using React with streaming agent interactions, AG-UI components, and HITL design patterns

• Implement real-time agent communication interfaces with streaming output, MCP elicitation flows, and event-driven notifications

• Design and expose REST APIs and webhook integrations for agent-to-system and system-to-system interactions

 

Azure AI Platform Engineering
• Deploy and manage AI workloads on Azure AI Foundry, Azure OpenAI Service, Azure Machine Learning, and AKS

• Design event-driven and serverless architectures leveraging Azure Functions, Event Grid, Service Bus, and Azure API Management

• Build scalable, resilient, cost-efficient cloud architectures aligned with Azure Solutions Architecture best practices

• Implement Infrastructure as Code (IaC) using Terraform; establish pipeline-as-code and policy-as-code practices across CI/CD workflows

• Containerize AI workloads using Docker and Kubernetes for portable, scalable deployment

 

LLM Integration & Enterprise Reliability
• Lead prompt engineering, evaluation, and optimization strategies for OpenAI GPT models, Anthropic Claude, and Azure-hosted models

• Implement RAG architectures using vector databases (Azure AI Search, PostgreSQL pgvector, Cosmos DB) and design extensible, evolvable schema and ontology models

• Focus on making enterprise AI systems reliable, accurate, controllable, and production-ready — especially when working with LLMs like OpenAI GPT models or Anthropic Claude models

• Design guardrails, output validation layers, and hallucination mitigation patterns for high-stakes enterprise workflows

Data Architecture — Relational, NoSQL & Graph

• Design and work across relational databases (PostgreSQL, SQL Server), NoSQL stores (Cosmos DB, MongoDB), and graph databases for knowledge graph and ontology-driven AI use cases

• Model extensible, evolvable schemas and domain ontologies that support AI reasoning, entity resolution, and semantic retrieval

 

Security & Identity
• Implement enterprise-grade security across AI systems: OAuth 2.0, Azure IAM, role-based and fine-grained access control (FGAC), managed identities, and credentials management

• Apply Azure security policies, RBAC, and least-privilege principles to AI platform components and agentic workflows

• Ensure secure handling of credentials, API keys, and secrets using Azure Key Vault and secure secrets management practices

 

AI-Native Engineering Practices
• Drive AI-assisted software engineering practices across the SDLC using copilots, autonomous coding agents, spec-driven development, and reusable engineering skills

• Leverage coding agents effectively across all SDLC phases — from requirements and design through development, testing, and deployment

• Help establish AI fluency standards and engineering productivity patterns across teams

• Contribute to internal AI accelerators, engineering frameworks, and delivery automation capabilities

• Enable engineering teams to effectively collaborate with AI systems while maintaining quality, governance, and reliability

 

Enterprise Governance & Responsible AI
• Implement responsible AI controls including observability, auditability, security, prompt protection, PII handling, and human oversight mechanisms

• Design enterprise-safe AI systems with governance, compliance, and reliability considerations built in from the ground up

• Establish patterns for AI system transparency, explainability, and accountability in regulated industry contexts

 

Technical Leadership & Modern Delivery
• Define AI engineering standards, design patterns, and best practices across the engineering organization

• Lead architecture reviews, code reviews, and technical roadmap planning for AI platform capabilities

• Mentor mid-level and junior engineers; foster a culture of AI-native engineering excellence

• Operate effectively in fast-moving, iterative AI delivery environments where experimentation, rapid prototyping, and production hardening coexist

• Balance innovation speed with engineering rigor, scalability, and maintainability

• Communicate complex AI concepts clearly to both engineering and business stakeholders

• Engage confidently with enterprise clients, architecture teams, and delivery leadership to shape AI solution direction

 

REQUIRED QUALIFICATIONS
Agentic AI & LLM Engineering

• 7+ years of software engineering experience with 3+ years in AI/ML or LLM-based systems

• Hands-on experience building production-grade agentic or multi-agent AI workflows

• Proficiency with GenAI agentic frameworks: LangGraph, Semantic Kernel, AutoGen, CrewAI, or LangChain

• Working knowledge of agentic protocols: MCP (Model Context Protocol), A2A (Agent-to-Agent), AG-UI, and CodeAct/Code Interpreter patterns

• Strong experience with context management strategies, agent skill design, agent evaluation, and agent harness construction

• Proficiency with OpenAI APIs (GPT-4o, function calling, Assistants API) and Anthropic Claude APIs

• RAG pipeline design: vector databases (Azure AI Search, PostgreSQL pgvector, Cosmos DB), chunking, embedding, and retrieval strategies

• Ability to pivot across agentic framework approaches and managed agent platforms as the ecosystem evolves

Full-Stack & API Engineering

• Full-stack experience with React; ability to build streaming agent interaction UIs, AG-UI components, and HITL design patterns

• Strong REST API and webhook design and implementation skills

• Proficiency in Python (intermediate level) and TypeScript for AI application and backend development

Cloud, Infrastructure & Architecture

• Strong Azure platform experience: Azure AI Foundry, Azure OpenAI, Azure ML, AKS, Azure Functions, API Management, Event Grid, Service Bus

• Infrastructure as Code using Terraform; pipeline-as-code and policy-as-code practices in CI/CD workflows

• Proficiency with containers (Docker, Kubernetes) for scalable AI workload deployment

• Ability to design and implement scalable, resilient, cost-efficient architectures on Azure

• Event-driven architecture and serverless architecture design and implementation

• Azure Solutions Architecture understanding across compute, networking, storage, security, and AI tiers

Data & Schema Design

• Experience with relational databases (PostgreSQL, SQL Server), NoSQL (Cosmos DB, MongoDB), and graph databases

• Ability to design extensible, evolvable schemas and domain ontologies that support AI reasoning and semantic retrieval

Security & Identity

• OAuth 2.0 implementation and Azure IAM/RBAC: permissions, policies, managed identities, and fine-grained access control (FGAC)

• Secure credentials management using Azure Key Vault and secrets management best practices

• Security-first mindset for AI systems: prompt protection, PII handling, data boundary enforcement

Engineering Practices

• Demonstrated ability to leverage coding agents and spec-driven development across all SDLC phases

• Strong GitHub Copilot and AI-assisted development tooling proficiency

• Experience leading technical teams and influencing engineering practices at an organizational level

 

NICE TO HAVE

• Experience designing and deploying Engineering Agent Skills that work alongside domain SMEs in human-AI collaborative workflows

• Wholesale insurance domain understanding: submission processing, broker/carrier workflows, market access, and underwriting operations

• Hands-on experience transitioning from custom agentic frameworks to managed agent platforms (Azure AI Agent Service, OpenAI Assistants, etc.)

• Experience with Databricks, MLflow, or Azure Databricks for data and model pipelines

• Prior work on document intelligence platforms (OCR, extraction, classification, IDP pipelines)

• Azure certifications (AI-102, DP-100, AZ-305) or relevant cloud AI credentials

• Contributions to open-source AI frameworks or published technical writing

• Experience working in startup or high-growth engineering environments

• Passion for AI-native engineering transformation and modern software delivery practices.